But not solely ill-defined queries that bias the explanation can ultimately lead to a false causality statement. At every stage in the process, a bias is induced (fig1, bottom), limiting the power of the causal statement, and increasing the risk for social misalignment or even IML failure. The definition of a faithful, thus true explanation, is non-trivial since there is no given ground truth label and it depends on the underlying model. While some biases are induced by design such as sampling procedure, model architecture, or query method selection, others are more unconscious such as various human biases. However, these biases do not necessarily prevent us from reaching a true causal statement [1]}. In fact, with statistics, there is a whole scientific discipline which concern is to organize, analyze, interpret, and present model and data utilizing limited methods inducing bias to reach a true causal statement.
